Paper Citation Record · LEDGER

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing

As of 7 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 2 inbound Pith citation observations for arXiv:2507.03211.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.03211 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:21:16.356784Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T13:06:53.051428Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-28T23:42:50.152858Z

Reference resolution

17 of 17 outbound references displayed

verified exact0
verified fuzzy2
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fccf1410-7c31-44c6-977d-546baa048b19 · outbound

This paper cites Decoupled Weight Decay Regularization.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing Decoupled Weight Decay Regularization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.394786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0aa050c-1b56-47b4-b83d-86c9194b1c7e · outbound

This paper cites An overview of gradient descent optimization algorithms.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing An overview of gradient descent optimization algorithms

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.492721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7aed5f28-74af-46a4-890b-ae4476c1af75 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.668547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14530e51-8e5e-451a-99f5-de37b00aa496 · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing Recursive deep models for semantic compositionality over a sentiment treebank

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.763454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36fa07b0-8e72-46d8-8899-5ff3eb152f43 · outbound

This paper cites ZO methods have found applications in black-box adversarial attacks (Chen et al., 2017), reinforcement learning (Salimans et al., 2017), and model-agnostic optimization.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing ZO methods have found applications in black-box adversarial attacks (Chen et al., 2017), reinforcement learning (Salimans et al., 2017), and model-agnostic optimization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:21:17.000090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 581c05ad-dbbf-4f3c-83a2-1aa0640af855 · outbound

This paper cites In particular, DDP must synchronize scalar gradients and coordinate consistent perturbation vectors across devices.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing In particular, DDP must synchronize scalar gradients and coordinate consistent perturbation vectors across devices

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:21:16.804754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9ddba0ac-9f5b-4591-b3cb-d9d95ace0f7b · outbound

This paper cites an unresolved cited work.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:21:16.617942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 293afdd3-4e1f-4fb5-98f8-8d0a1f982744 · outbound

This paper cites The Llama 3 Herd of Models.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing The Llama 3 Herd of Models

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.133885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7da893e6-5b98-48e3-b43b-6c5f3c50c2e6 · outbound

This paper cites ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.841417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a06fc0e6-568d-47da-b42d-51f35af5215c · outbound

This paper cites Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.588830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation deee3e0f-4e78-4a7e-bef0-a277ac895b5f · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:14.932988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fe78b62-90f1-494d-a3cf-e3b6680bd812 · outbound

This paper cites PyTorch Distributed: Experiences on Accelerating Data Parallel Training.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.223770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation caba061a-204d-4bef-b5b0-e70479e1190a · outbound

This paper cites DeepSeek-V3 Technical Report.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing DeepSeek-V3 Technical Report

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.282175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e88e99eb-a13e-438e-bd1a-df8983cf1165 · outbound

This paper cites Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:16.047255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36e22ed4-9273-4f71-80ce-5028e4437784 · outbound

This paper cites Online convex optimization in the bandit setting: gradient descent without a gradient.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.038451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e09c0a3-b3fc-4994-b959-acc47b44fc80 · outbound

This paper cites Language models are few-shot learners.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing Language models are few-shot learners

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:14.859675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ad97cb6-1986-4504-a2c4-7730260c2143 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing OPT: Open Pre-trained Transformer Language Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.944163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation a28c8d33-36a1-47d7-a494-a54635e809f4 · inbound

Algorithmic Recourse of In-Context Learning for Tabular Data cites this paper.

Algorithmic Recourse of In-Context Learning for Tabular Data DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:42:50.154714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 84dfa303-80d4-4ab7-8811-6890ad5b5979 · inbound

Algorithmic Recourse of In-Context Learning for Tabular Data cites this paper.

Algorithmic Recourse of In-Context Learning for Tabular Data DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-02T13:06:53.051428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.